content: /*
   * Copyright 2016 The TensorFlow Authors. All Rights Reserved.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *       http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.tensorflow.demo;
  
  import android.graphics.Bitmap;
  import android.graphics.Bitmap.Config;
  import android.graphics.Canvas;
  import android.graphics.Matrix;
  import android.graphics.Paint;
  import android.graphics.Typeface;
  import android.media.ImageReader.OnImageAvailableListener;
  import android.os.SystemClock;
  import android.util.Size;
  import android.util.TypedValue;
  import android.view.Display;
  import android.view.Surface;
  import java.util.List;
  import java.util.Vector;
  import org.tensorflow.demo.OverlayView.DrawCallback;
  import org.tensorflow.demo.env.BorderedText;
  import org.tensorflow.demo.env.ImageUtils;
  import org.tensorflow.demo.env.Logger;
  import org.tensorflow.demo.R; // Explicit import needed for internal Google builds.
  
  public class ClassifierActivity extends CameraActivity implements OnImageAvailableListener {
    private static final Logger LOGGER = new Logger();
  
    protected static final boolean SAVE_PREVIEW_BITMAP = false;
  
    private ResultsView resultsView;
  
    private Bitmap rgbFrameBitmap = null;
    private Bitmap croppedBitmap = null;
    private Bitmap cropCopyBitmap = null;
  
    private long lastProcessingTimeMs;
  
    // These are the settings for the original v1 Inception model. If you want to
    // use a model that's been produced from the TensorFlow for Poets codelab,
    // you'll need to set IMAGE_SIZE = 299, IMAGE_MEAN = 128, IMAGE_STD = 128,
    // INPUT_NAME = "Mul", and OUTPUT_NAME = "final_result".
    // You'll also need to update the MODEL_FILE and LABEL_FILE paths to point to
    // the ones you produced.
    //
    // To use v3 Inception model, strip the DecodeJpeg Op from your retrained
    // model first:
    //
    // python strip_unused.py \
    // --input_graph=<retrained-pb-file> \
    // --output_graph=<your-stripped-pb-file> \
    // --input_node_names="Mul" \
    // --output_node_names="final_result" \
    // --input_binary=true
    private static final int INPUT_SIZE = 224;
    private static final int IMAGE_MEAN = 117;
    private static final float IMAGE_STD = 1;
    private static final String INPUT_NAME = "input";
    private static final String OUTPUT_NAME = "output";
  
  
    private static final String MODEL_FILE = "file:///android_asset/tensorflow_inception_graph.pb";
    private static final String LABEL_FILE =
        "file:///android_asset/imagenet_comp_graph_label_strings.txt";
  
  
    private static final boolean MAINTAIN_ASPECT = true;
  
    private static final Size DESIRED_PREVIEW_SIZE = new Size(640, 480);
  
  
    private Integer sensorOrientation;
    private Classifier classifier;
    private Matrix frameToCropTransform;
    private Matrix cropToFrameTransform;
  
  
    private BorderedText borderedText;
  
  
    @Override
    protected int getLayoutId() {
      return R.layout.camera_connection_fragment;
    }
  
    @Override
    protected Size getDesiredPreviewFrameSize() {
      return DESIRED_PREVIEW_SIZE;
    }
  
    private static final float TEXT_SIZE_DIP = 10;
  
    @Override
    public void onPreviewSizeChosen(final Size size, final int rotation) {
      final float textSizePx = TypedValue.applyDimension(
          TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());
      borderedText = new BorderedText(textSizePx);
      borderedText.setTypeface(Typeface.MONOSPACE);
  
      classifier =
          TensorFlowImageClassifier.create(
              getAssets(),
              MODEL_FILE,
              LABEL_FILE,
              INPUT_SIZE,
              IMAGE_MEAN,
              IMAGE_STD,
              INPUT_NAME,
              OUTPUT_NAME);
  
      previewWidth = size.getWidth();
      previewHeight = size.getHeight();
  
      sensorOrientation = rotation - getScreenOrientation();
      LOGGER.i("Camera orientation relative to screen canvas: %d", sensorOrientation);
  
      LOGGER.i("Initializing at size %dx%d", previewWidth, previewHeight);
      rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Config.ARGB_8888);
      croppedBitmap = Bitmap.createBitmap(INPUT_SIZE, INPUT_SIZE, Config.ARGB_8888);
  
      frameToCropTransform = ImageUtils.getTransformationMatrix(
          previewWidth, previewHeight,
          INPUT_SIZE, INPUT_SIZE,
          sensorOrientation, MAINTAIN_ASPECT);
  
      cropToFrameTransform = new Matrix();
      frameToCropTransform.invert(cropToFrameTransform);
  
      addCallback(
          new DrawCallback() {
            @Override
            public void drawCallback(final Canvas canvas) {
              renderDebug(canvas);
            }
          });
    }
  
    @Override
    protected void processImage() {
      rgbFrameBitmap.setPixels(getRgbBytes(), 0, previewWidth, 0, 0, previewWidth, previewHeight);
      final Canvas canvas = new Canvas(croppedBitmap);
      canvas.drawBitmap(rgbFrameBitmap, frameToCropTransform, null);
  
      // For examining the actual TF input.
      if (SAVE_PREVIEW_BITMAP) {
        ImageUtils.saveBitmap(croppedBitmap);
      }
      runInBackground(
          new Runnable() {
            @Override
            public void run() {
              final long startTime = SystemClock.uptimeMillis();
              final List<Classifier.Recognition> results = classifier.recognizeImage(croppedBitmap);
              lastProcessingTimeMs = SystemClock.uptimeMillis() - startTime;
              LOGGER.i("Detect: %s", results);
              cropCopyBitmap = Bitmap.createBitmap(croppedBitmap);
              if (resultsView == null) {
                resultsView = (ResultsView) findViewById(R.id.results);
              }
              resultsView.setResults(results);
              requestRender();
              readyForNextImage();
            }
          });
    }
  
    @Override
    public void onSetDebug(boolean debug) {
      classifier.enableStatLogging(debug);
    }
  
    private void renderDebug(final Canvas canvas) {
      if (!isDebug()) {
        return;
      }
      final Bitmap copy = cropCopyBitmap;
      if (copy != null) {
        final Matrix matrix = new Matrix();
        final float scaleFactor = 2;
        matrix.postScale(scaleFactor, scaleFactor);
        matrix.postTranslate(
            canvas.getWidth() - copy.getWidth() * scaleFactor,
            canvas.getHeight() - copy.getHeight() * scaleFactor);
        canvas.drawBitmap(copy, matrix, new Paint());
  
        final Vector<String> lines = new Vector<String>();
        if (classifier != null) {
          String statString = classifier.getStatString();
          String[] statLines = statString.split("\n");
          for (String line : statLines) {
            lines.add(line);
          }
        }
  
        lines.add("Frame: " + previewWidth + "x" + previewHeight);
        lines.add("Crop: " + copy.getWidth() + "x" + copy.getHeight());
        lines.add("View: " + canvas.getWidth() + "x" + canvas.getHeight());
        lines.add("Rotation: " + sensorOrientation);
        lines.add("Inference time: " + lastProcessingTimeMs + "ms");
  
        borderedText.drawLines(canvas, 10, canvas.getHeight() - 10, lines);
      }
    }
  }
  